• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.6
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• pp.517-524
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• 2017

The objective of this research is to improve the efficiency of data collection from many machine components on smart factory floors using IoT(Internet of things) techniques and cloud platform, and to make it easy to update outdated diagnostic schemes through online deployment methods from cloud resources. The short-term analysis is implemented by a micro-controller, and it includes machine-learning algorithms for inferring snapshot information of the machine components. For long-term analysis, time-series and high-dimension data are used for root cause analysis by combining a cloud platform and multivariate analysis techniques. The diagnostic results are visualized in a web-based display dashboard for an unconstrained user access. The implementation is demonstrated to identify its performance in data acquisition and analysis for rotating machinery.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.53-59
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• 2008

Recently, vision-based hand gesture recognition techniques have been developed for assisting elderly and disabled people to control home appliances. Frequently occurred problems which lower the hand gesture recognition rate are due to the inter-person variation and intra-person variation. The recognition difficulty caused by inter-person variation can be handled by using user dependent model and model selection technique. And the recognition difficulty caused by intra-person variation can be handled by using fuzzy logic. In this paper, we propose multivariate fuzzy decision tree learning and classification method for a hand motion recognition system for multiple users. When a user starts to use the system, the most appropriate recognition model is selected and used for the user.

• Journal of Korean Neurosurgical Society
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• v.62 no.5
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• pp.526-535
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• 2019

Objective : While the risk of aneurysmal rebleeding induced by catheter cerebral angiography is a serious concern and can delay angiography for a few hours after a subarachnoid hemorrhage (SAH), current angiographic technology and techniques have been much improved. Therefore, this study investigated the risk of aneurysmal rebleeding when using a recent angiographic technique immediately after SAH. Methods : Patients with acute SAH underwent immediate catheter angiography on admission. A four-vessel examination was conducted using a biplane digital subtraction angiography (DSA) system that applied a low injection rate and small volume of a diluted contrast, along with appropriate control of hypertension. Intra-angiographic aneurysmal rebleeding was diagnosed in cases of extravasation of the contrast medium during angiography or increased intracranial bleeding evident in flat-panel detector computed tomography scans. Results : In-hospital recurrent hemorrhages before definitive treatment to obliterate the ruptured aneurysm occurred in 11 of 266 patients (4.1%). Following a univariate analysis, a multivariate analysis using a logistic regression analysis revealed that modified Fisher grade 4 was a statistically significant risk factor for an in-hospital recurrent hemorrhage (p=0.032). Cerebral angiography after SAH was performed on 88 patients ${\leq}3$ hours, 74 patients between 3-6 hours, and 104 patients >6 hours. None of the time intervals showed any cases of intra-angiographic rebleeding. Moreover, even though the DSA ${\leq}3$ hours group included more patients with a poor clinical grade and modified Fisher grade 4, no case of aneurysmal rebleeding occurred during erebral angiography. Conclusion : Despite the high risk of aneurysmal rebleeding within a few hours after SAH, emergency cerebral angiography after SAH can be acceptable without increasing the risk of intra-angiographic rebleeding when using current angiographic techniques and equipment.

• Yonsei Medical Journal
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• v.59 no.9
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• pp.1049-1056
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• 2018

Purpose: Local recurrence is the most common cause of failure in retroperitoneal soft tissue sarcoma patients after surgical resection. Postoperative radiotherapy (PORT) is infrequently used due to its high complication risk. We investigated the efficacy of PORT using modern techniques in patients with retroperitoneal soft tissue sarcoma. Materials and Methods: Eighty patients, who underwent surgical resection for non-metastatic primary retroperitoneal soft tissue sarcoma at the Yonsei Cancer Center between 1994 and 2015, were retrospectively reviewed. Thirty-eight (47.5%) patients received PORT: three-dimensional conformal radiotherapy in 29 and intensity-modulated radiotherapy in nine patients. Local failure-free survival (LFFS), overall survival (OS), and RT-related toxicities were investigated. Results: Median follow-up was 37.1 months (range, 5.8-207.9). Treatment failure occurred in 47 (58.8%) patients including local recurrence in 33 (41.3%), distant metastasis in eight (10%), and both occurred in six (7.5%) patients. The 2-year and 5-year LFFS rates were 63.9% and 47.9%, respectively. The 2-year and 5-year OS rates were 87.5% and 71.1%. The 5-year LFFS rate was significantly higher in PORT group than in no-PORT group (74.2% vs. 24.3%, p<0.001). In multivariate analysis, PORT was the only independent prognostic factor for LFFS. However, there was no significant correlation between RT dose and LFFS. OS showed no significant difference between the two groups. Grade ${\leq}2$ acute toxicities were observed in 63% of patients, but no acute toxicity ${\geq}$ grade 3 was observed. Conclusion: PORT using modern technique markedly reduced local recurrence in retroperitoneal sarcoma patients, with low toxicity. The optimal RT technique, in terms of RT dose and target volume, should be further investigated.

• International Journal of Reliability and Applications
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• v.2 no.3
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• pp.209-240
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• 2001

Computer-based simulation and analysis is used extensively in engineering for a variety of tasks. Despite the steady and continuing growth of computing power and speed, the computational cost of complex high-fidelity engineering analyses and simulations limit their use in important areas like design optimization and reliability analysis. Statistical approximation techniques such as design of experiments and response surface methodology are becoming widely used in engineering to minimize the computational expense of running such computer analyses and circumvent many of these limitations. In this paper, we compare and contrast five experimental design types and four approximation model types in terms of their capability to generate accurate approximations for two engineering applications with typical engineering behaviors and a wide range of nonlinearity. The first example involves the analysis of a two-member frame that has three input variables and three responses of interest. The second example simulates the roll-over potential of a semi-tractor-trailer for different combinations of input variables and braking and steering levels. Detailed error analysis reveals that uniform designs provide good sampling for generating accurate approximations using different sample sizes while kriging models provide accurate approximations that are robust for use with a variety of experimental designs and sample sizes.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1172-1180
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• 2004

Recently, the gene expression data, product of high-throughput technology, appeared in earnest and the studies related with it (so-called bioinformatics) occupied an important position in the field of biological and medical research. The microarray is a revolutionary technology which enables us to monitor several thousands of genes simultaneously and thus to gain an insight into the phenomena in the human body (e.g. the mechanism of cancer progression) at the molecular level. To obtain useful information from such gene expression measurements, it is essential to analyze the data with appropriate techniques. However the high-dimensionality of the data can bring about some problems such as curse of dimensionality and singularity problem of matrix computation, and hence makes it difficult to apply conventional data analysis methods. Therefore, the development of method which can effectively treat the data becomes a challenging issue in the field of computational biology. This research focuses on the gene selection and classification for cancer subtype discrimination based on gene expression (microarray) data.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.18-18
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• 1996

This paper reviews some of the most prominent and promising areas of chemical process control both in relations to batch and continuous processes. These areas include the modeling, optimization, control and monitoring of chemical processes and entire plants. Most of these areas explicitly utilize a model of the process. For this purpose the types of models used are examined in some detail. These types of models are categorized in knowledge-driven and datadriven classes. In the areas of modeling and optimization, attention is paid to batch reactors using the Tendency Modeling approach. These Tendency models consist of data- and knowledge-driven components and are often called Gray or Hybrid models. In the case of continuous processes, emphasis is placed in the closed-loop identification of a state space model and their use in Model Predictive Control nonlinear processes, such as the Fluidized Catalytic Cracking process. The effective monitoring of multivariate process is examined through the use of statistical charts obtained by the use of Principal Component Analysis (PMC). Static and dynamic charts account for the cross and auto-correlation of the substantial number of variables measured on-line. Centralized and de-centralized chart also aim in isolating the source of process disturbances so that they can be eliminated. Even though significant progress has been made during the last decade, the challenges for the next ten years are substantial. Present progress is strongly influenced by the economical benefits industry is deriving from the use of these advanced techniques. Future progress will be further catalyzed from the harmonious collaboration of University and Industrial researchers.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.575-587
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• 2015

The development in data collection techniques results in high dimensional data sets, where discrimination is an important and commonly encountered problem that are crucial to resolve when high dimensional data is heterogeneous (non-common variance covariance structure for classes). An example of this is to classify microbial habitat preferences based on codon/bi-codon usage. Habitat preference is important to study for evolutionary genetic relationships and may help industry produce specific enzymes. Most classification procedures assume homogeneity (common variance covariance structure for all classes), which is not guaranteed in most high dimensional data sets. We have introduced regularized elimination in partial least square coupled with QDA (rePLS-QDA) for the parsimonious variable selection and classification of high dimensional heterogeneous data sets based on recently introduced regularized elimination for variable selection in partial least square (rePLS) and heterogeneous classification procedure quadratic discriminant analysis (QDA). A comparison of proposed and existing methods is conducted over the simulated data set; in addition, the proposed procedure is implemented to classify microbial habitat preferences by their codon/bi-codon usage. Five bacterial habitats (Aquatic, Host Associated, Multiple, Specialized and Terrestrial) are modeled. The classification accuracy of each habitat is satisfactory and ranges from 89.1% to 100% on test data. Interesting codon/bi-codons usage, their mutual interactions influential for respective habitat preference are identified. The proposed method also produced results that concurred with known biological characteristics that will help researchers better understand divergence of species.

• Journal of the Speleological Society of Korea
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• no.93
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• pp.13-25
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• 2009

This study, which includes case studies that provide information of cave tour resources, considered a variety of selected variables of the internal and external parts of caves with the expanded factors of the academic classification in caves. It uses the cluster analysis, one of the multivariate analysis techniques, and applied the results for review. As a result, public caves can present multiple classification criteria according to the factors of the surrounding area's human environment. The result, classified by the region in public caves, is derived from this study.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.137-148
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• 2001

Compositional data found in many practical applications consist of non-negative vectors of proportions with the constraint which the sum of the elements of each vector is unity. It is well-known that the statistical analysis of compositional data suffers from the unit-sum constraint. Moreover, the non-linear pattern frequently displayed by the data does not facilitate the application of the linear multivariate techniques such as principal component analysis. In this paper we develop new type of principal component analysis for compositional data using Box-Cox contrast transformation. Numerical illustrations are provided for comparative purpose.